Trained Machine Learning Model For Estimating Structure Feature Measurements

ABSTRACT

A computer system trains a machine learning model to estimate a real-world measurement of a feature of a structure. The machine learning model is trained using a plurality of digital image sets, wherein each image set depicts a particular structure, and a plurality of measurements, wherein each measurement is a measurement of a feature of a particular structure. After the machine learning model is trained, it is used to estimate a measurement of a feature of a particular structure depicted in a particular image set.

FIELD OF THE INVENTION

The present invention relates to machine learning models, and morespecifically, to training a machine learning model to estimate ameasurement of a structure feature depicted in a set of digital images.

BACKGROUND

Three-dimensional (3D) models of a building may be generated based ontwo-dimensional (2D) digital images taken of the building. The digitalimages may be taken via aerial imagery, specialized-camera equippedvehicles, or by a user with a camera. The 3D building model is a digitalrepresentation of the physical, real-world building. An accurate 3Dmodel may be used to derive various building measurements or to estimatedesign and renovation costs.

However, generating a 3D model of a building requires significant timeand resources. If only a particular measurement, or set of measurements,is needed, it would be inefficient to generate a 3D model of the wholebuilding in order to derive the particular measurement or measurements.Thus, an efficient method for estimating building feature measurementsfrom digital images, without generating a full 3D model of the building,is desired.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system configured to perform thefunctions described herein;

FIG. 2 illustrates an example process for training and using a machinelearning model to estimate a measurement of a feature;

FIG. 3 illustrates an example image set;

FIG. 4 is a block diagram illustrating a computer system that may beused to implement the techniques described herein.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

General Overview

Techniques are described herein for training a machine learning model toestimate a measurement for a feature of a real-world structure. As usedherein, the term “structure” refers to anything with a feature whosemeasurements may be estimated based on images depicting the structure. Astructure may be a man-made structure, such as buildings, walls, fences,swimming pools, and etc. A measurement is the actual real-world distanceassociated with a real-world feature (e.g. length of a roof) or anyvalue that is based on such a distance (e.g. the area of a roof). Afeature is any element, portion, or attribute of a real-world structure.Measurements of a feature include, for example, the square footage of aroof, the perimeter length of a pool, the area of a wall, etc.

For the purpose of explanation, examples shall be given herein where thefeatures whose measurements are being estimated are features ofbuildings. However, the techniques described herein are not limited toany particular type of structure or feature.

In an embodiment, to train the machine learning model, the machinelearning model is provided a plurality of digital image sets and aplurality of real-world measurements. Each digital image set depicts aparticular structure or one or more portions of a particular structure.Each real-world measurement corresponds to a particular image set and isan actual measurement of a feature of the depicted structure or afeature derived from a structure. For example, each image set may depicta particular building or a part of the particular building, and thecorresponding measurement is the roof area of the particular building.

In an embodiment, using the plurality of image sets and the plurality ofreal-world measurements, the machine learning model is trained toestimate a particular measurement for a particular type of structure. Insome embodiments, the machine learning model is trained to determine aparticular type of structure and estimate a measurement based on thetype of structure. In other embodiments, the machine learning model istrained to estimate one or more variables that are used to calculate theparticular measurement.

After the machine learning model is trained, the machine learning modelmay be used to estimate a measurement of the real-world feature of astructure depicted in a particular image set.

Machine Learning Model

FIG. 1 illustrates an example computer system 110 that is configured toperform the techniques described herein. In the illustrated embodiment,computer system 110 is communicatively coupled via a network 102 to adata server 104 and an image capture device 106. Example computer system110 may include, or communicate with, other devices including computingdevices, image capture devices, databases and other data storagedevices, and display devices, according to embodiments. For example, aplurality of image capture devices and data servers may becommunicatively coupled to computer system 110. As another example, oneor more of the services attributed to computer system 110 herein may berun on other computer systems that are communicatively coupled tonetwork 102 or run internally on different parts of a single computersystem.

Computer system 110 may be implemented by any type of computing devicethat is communicatively coupled to network 102. Example implementationsof computer system 110 include, but are not limited to, workstations,personal computers, laptops, connected devise such as a mobile phone ortablet, multi-processor systems, and the like. Although FIG. 1 shows asingle element, computer system 110 may comprise one or more computers,such as a server cluster, and the computer system 110 may be located inone or more physical locations.

In the illustrated embodiment, computer system 110 is communicativelycoupled to a database 120, which stores images received at computersystem 110. Database 120 may also store metadata associated with eachimage. In one embodiment, database 120 is a data storage subsystemconsisting of programs and data that is stored on any suitable storagedevice such as one or more hard disk drives, memories, or any otherelectronic digital data recording device configured to store data.Although database 120 is depicted as a single device in FIG. 1, database120 may span multiple devices located in one or more physical locations.Database 120 may be internal or external to computer system 110.

In some embodiments, computer system 110 may download, obtain, orreceive digital images from various sources, such as data server 104 orimage capture device 106. Example sources include image capture devices,remote computing devices, such as mobile phones or server computers, andsatellite image providers, such as National Aeronautics and SpaceAdministration (NASA), United States Geological Survey (USGS), andGoogle Earth.

Data server 104 may be any computing device, including but not limitedto: servers, racks, work stations, personal computers, laptops, Internetappliances, wireless devices, wired devices, multi-processor systems,and the like. Although FIG. 1 shows a single element, the data server104 broadly represents one or more multiple server computers, such as aserver cluster, and data server 104 may be located in one or morephysical locations. Data server 104 may also represent one or morevirtual computing instances that execute using one or more computers ina datacenter such as a virtual server farm. Data server 104 may receiveor obtain digital images from an image capture device 106, users, otherdata servers, or other sources. Data server 104 may also transmitdigital images to computer system 110.

Image capture device 106 may be any device that can capture or recordimages and videos. For example, image capture device 106 may be anycomputing device with a built-in camera or a communicatively coupleddigital camera. Example image capture devices include mobile phones,tablets, a computer with a webcam, a drone, or a specializedcamera-equipped vehicle. In the illustrated embodiment, image capturedevice 106 includes one or more sensors 108. Sensor data from sensors108 may be stored in association with digital images captured by imagecapture device 106. Additionally or alternatively, sensor data fromsensors 108 may be transmitted independently of digital images capturedby image capture device 106. Example sensors include, but are notlimited to, global positioning system (GPS), accelerometers, altimeters,gyroscopes, magnetometers, temperature sensors, light sensors, andproximity sensors. The number and types of sensor data associated with adigital image may vary depending on the image capture device and theparticular implementation.

In the illustrated embodiment, image capture device 106 is connected todata server 104 and computer system 110 via network 102. Image capturedevice 106 may be configured to transmit images directly to a dataserver 104 or to computer system 110.

Computer system 110 further comprises training instructions 112 andexecution instructions 114. Training instructions 112 comprise one ormore instructions which, when executed by computer system 110, causecomputer system 110 to train a machine learning model using a pluralityof image sets, each of which comprise one or more digital images, and aplurality of measurements. Execution instructions 114 comprise one ormore instructions which, when executed by computer system 110, causecomputer system 110 to use the trained machine learning model to computeone or more measurements from a particular image set.

Machine Learning Model

Training instructions 112 and execution instruction 114, when executed,train and use a machine learning model. Various machine learningalgorithms and structures are available to implement the machinelearning model. Example machine learning algorithms include artificialneural networks, deep neural networks, convolution neural networks,recursive neural networks, classifiers, and other supervised orunsupervised machine learning algorithms. The proposed method and systemmay be implemented using any suitable machine learning algorithm orarchitectures.

In an embodiment, the machine learning model is a convolutional neuralnetwork (CNN). An example CNN architecture includes Microsoft's ResNET,described by Kaiming He, Xiangyu Zhang, Shaoquing Ren, and Jian Sun.“Deep Residual Learning for Image Recognition.” arXiv:1512.00385.https://arxiv.org/pdf/1512.03385.pdf.

A neural network is a machine learning technique that utilizes a networkof learning units, also referred to as neural nodes. The neural nodesare trained to convert an input, such as an image, into correspondingoutput signals, such as a feature measurement. In a convolutional neuralnetwork, the plurality of neural nodes are arranged in a plurality oflayers. Each node receives input from one or more nodes in layers belowit, and passes data to one or more nodes in layers above it.

Each neural node may be associated with a weight that is applied to thedata it receives to generate output to pass to a subsequent neural node.Each neural node may also be associated with a threshold value, whereinoutput is passed to one or more subsequent nodes if the threshold valueis met.

Digital Image Sets

In an embodiment, the input provided to a machine learning modelincludes an image set comprising one or more digital images. The imageset may depict a particular structure, and each digital image of theimage set may depict a different view of the particular structure. Forexample, a first digital image may depict a front view of a structureand a second digital image may depict a top-down view of the samestructure.

A digital image may be an orthographic image, an oblique image, or alateral image. An orthographic image is an image taken from overhead,such as a satellite image. An orthographic image depicts a top-down viewof a structure.

An oblique image is an aerial image taken at an angle, typically aboutforty-five degrees. An oblique image depicts a portion of the top of astructure and a portion of the side(s) of the structure.

A lateral image is an image that substantially depicts one or more sidesof a single building, such as a front or back view, side view, or acorner view. In an embodiment, a lateral image may be a ground levelimage. A ground level image is an image taken at or near ground levelfacing a structure. Additionally, a lateral photo may be captured athigher levels using, for example an image capture device mounted on astick or on a drone.

In an embodiment, an image set comprises one or more images of the sameimage type. For example, an image set may comprise a single orthographicimage. As another example, an image set may comprise a plurality oflateral images.

In other embodiments, an image set comprises a combination of imagetypes. For example, an image set may comprise an orthographic image andone or more lateral images.

Each digital image may also include metadata associated with the digitalimage. The metadata may be stored with the digital image, provided tothe machine learning model in association with the digital image, orprovided separately but in association with the digital image. Themetadata indicates information related to the digital image or toobjects depicted in the image. For example, metadata may indicate thelocation where the digital image was captured or a sequence numberindicating an image capture sequence. As another example, a user orother data source may provide information related to a structuredepicted in the digital image, such as whether particular elements arepresent in the digital image, dimensions of particular elements of thestructure, the structure type, and etc. The information may be stored asmetadata associated with the image. In an embodiment, metadataassociated with a digital image includes sensor data from the imagecapture device that captured the digital image.

Training the Machine Learning Model

In an embodiment, training a machine learning model comprises providingthe machine learning model with a set of training data. The trainingdata comprises a plurality of input data and a plurality ofcorresponding output data. The machine learning model uses the traininginputs and the training outputs to infer a method for correlating theinput to the output. Additionally, output generated by training themachine learning model may be provided as training inputs for adifferent machine learning model or for iteratively training the samemachine learning model.

In an embodiment, the machine learning model is a neural network whereineach neural node is associated with a weight and a threshold value.Training the machine learning model comprises determining a weight and athreshold value for each neural node such that the training inputgenerates output similar to the provided training output. Additionally,the output generated by training the machine learning model may bestored in association with the training input. The stored output may beused to generate a confidence level for future outputs generated by themachine learning model.

In an embodiment, the training input comprises a plurality of digitalimage sets and the training output comprises a plurality ofcorresponding measurements. The measurements provided as training dataare the type(s) of measurements that the machine learning model is beingtrained to estimate. Based on the training data, the machine learningmodel is trained to estimate a particular measurement when provided aninput image set.

Each image set depicts a particular structure. In an embodiment, eachimage set of the plurality of image sets depicts the same type ofstructure. The plurality of measurements correspond to measurements of afeature shared by the type of structure. Additionally or alternatively,the plurality of measurements may correspond to measurements of afeature related to the type of structure or related to a feature sharedby the type of structure.

For example, each image set in the training data may depict a window,and each measurement may be the height of the window depicted in thecorresponding image set. As another example, each image set may depictwalls, and the measurement may be the volume of the wall.

In some embodiments, the plurality of image sets depict different typesof structures. The machine learning model may be trained to determinethe type of structure and the measurement associated with the type ofstructure. For example, assume the plurality of image sets include bothimage sets that depict windows and image sets that depict walls. Themachine learning model may determine if an image set depicts a window ora wall. If the machine learning model determines the image set depicts awindow, then the machine learning model estimates the height of thewindow. If the machine learning model determines the image set depicts awall, then the machine learning model estimates the volume of the wall.

In an embodiment, the machine learning model is trained to estimate oneor more component variables that are used to calculate a measurement.For example, the volume of a wall is calculated by multiplying the areaof the base of the wall with the height of the wall. The machinelearning model may be trained to estimate either the area or the height.Alternately, the machine learning model may be trained to output bothvalues, the area and the height.

Additionally, the outputs of a machine learning model may be used tocalculate additional feature measurements. For example, a machinelearning output that includes the measurement of a roofline for a sideof a building may be used as an input to calculate the length of agutter for the same side of the building. Similarly, a guttermeasurement for the side of the building may be provided as an input toa machine learning model, in addition to other associated metadata, tocalculate the roofline for the side of the building.

In other embodiments, the machine learning model is trained to estimatethe measurement directly. The machine learning model may be provided aformula used to calculate the measurement. The machine learning model istrained to estimate the measurement based on the formula. In the aboveexample, rather than estimating the area and/or the height of the wall,the machine learning model is trained to estimate the volume of the walldirectly.

The techniques are described using a single machine learning model, butin other embodiments may be used with any number of machine learningmodels. For example, in the example above, a first machine learningmodel may be trained to estimate the area of a wall, and a secondmachine learning model may be trained to estimate the length of thewall. As another example, a first machine learning model may be trainedto identify the type of structure, and one or more machine learningmodel may be trained to estimate a measurement of a feature of one ormore corresponding types of structures.

As described above, each image set may comprises one or more digitalimages of one or more image types. The number and types of images in animage set may depend on the type of measurement being estimated. Forexample, if the machine learning model is trained to estimate the lengthof a wall, each image set may comprise one or more ground-level imagesof a wall. If the machine learning model is trained to estimate the areaof the base of the wall, each image set may comprise one or moreorthographic images of a wall. If the machine learning model is trainedto estimate the volume of the wall, each image set may comprise one ormore orthographic images and one or more ground-level images.

In an embodiment, rather than providing the entire digital image, eachimage of an image set comprises only metadata describing the image. Forexample, the metadata may include sensor data from when the image wascaptured and information describing the structure depicted in the image.

Normalizing Digital Images

In an embodiment, one or more digital images in an image set arenormalized before being provided to the machine learning model astraining input. Normalization reduces the number of independentvariables or features in the images, which increases the accuracy of thetrained machine learning model.

In an embodiment, normalizing an image comprises normalizing the scale.Normalizing the scale of the image scales the image such that pixelscorrespond to the same real-world metric distance. Scaling the image maycomprise one or more of cropping, stretching, or re-sizing the image.

As an example, an orthographic image may be captured at a particulargeographic coordinate and at a particular height range. For example, anorthographic image at height level 24 may correspond to a height of 100to 150 feet from the ground. Thus, even though each orthographic imageat level 24 was taken from the same height level, the actual height, andthus the image scale, varies between images. The orthographic images maybe scale normalized such that each scaled image depicts the same numberof metric feet per pixel.

For example, starting from the center of an image, the image may becropped to 50 feet out, resulting in a digital image that depicts a 100feet by 100 feet real-world area. The cropped image may be scaled to 200by 200 pixels, so that pixels in each scaled image represent the samemetric distance, 0.5 feet by 0.5 feet.

In an embodiment, normalizing the image comprises rotating the image.Images of the same type may be rotates such that particular featuresface a particular direction, align with a particular edge of the image,or are otherwise consistent across the images.

As an example, a plurality of orthogonal images may each depict atop-down view of a structure. The structure may be at a different anglein each picture. The images may be rotated so that the front of thestructure, or the side designated as the front of the structure, facesthe same direction.

As another example, a plurality of ground-level images may each depictthe front of a structure. Each ground-level image may be rotated so thatthe base of the structure is parallel to the bottom of the image.

In some embodiments, a machine learning model is trained to normalizeimages or to perform calculations related to normalizing images. Forexample, a machine learning model may be trained to calculate thecurrent scale of an image in order to determine how the image needs tobe scaled.

In some embodiments, normalizing an image is based, in part, on metadataassociated with the image or with other images in the image set. Forexample, orthographic images may be rotated so that the front of thestructure depicted in each orthographic image is facing north.Determining the direction the front of the structure is facing in theoriginal image may be based on a ground level image from the same imageset. The ground level image may include metadata indicating azimuthinformation. Azimuth indicates which direction the image capture devicewas facing, relative to north, when the digital image was captured. Bycorrelating the azimuth information with the structure depicted in theground level image and the orthographic image, the degree of rotationneeded in order for the front of the structure in the orthographic imageto face north can be calculated.

In an embodiment, the steps for normalizing any particular image isbased on the type of image. For example, assume an image set comprisesan orthographic image and a ground-level image. The orthographic imagemay be scaled and the ground-level image may be rotated.

In an embodiment, one or more images in an image set may be normalizedand one or more images in an image set are not normalized. Referring tothe above example, orthographic images may be scaled while ground-levelimages may not be normalized.

In an embodiment, the steps for normalizing an image is based on thetype of structure. For example, an orthographic image of a building maybe rotated so that the front of the building faces north, while anorthographic image of a pool is not rotated.

Removing Low-Quality Digital Images

In an embodiment, low quality images are removed from the plurality ofimage sets provided to the machine learning model as training data. Insome embodiments, if an image set includes a low quality image, theimage set is removed from the plurality of image sets provided to themachine learning model as training data. Removing low quality digitalimages, or image sets with low quality images, increases the overallquality of the training data, which increases the accuracy of thetrained machine learning model.

An image may be considered a low quality image, for example, if theimage has too much noise, if the image is too dark, if the structuredepicted in the image is too occluded, if a structure is not depicted inthe image, if the structure depicted in the image is not an expectedstructure type, if a particular feature is too occluded, or if aparticular feature is not depicted in the image. As referred to herein,“occluded” refers to an object, or at least a portion of the object, inan image being obscured, covered, hidden, cut off, or otherwise notfully visible within the image. Additionally, an image may be consideredlow quality if certain features cannot be detected in the image. Forexample, a machine learning model may be trained to detect corners andthe image includes a corner of a building, but the image does not depictthe portion of the corner in contact with the ground or the supportingwall.

In an embodiment, a classifier is trained to determine one or morefeatures of a low quality image. A classifier is a type of machinelearning model that receives an input and outputs a classification. Aclassification may indicate, for example, the type of structure orfeature depicted in an image, whether an image is too dark or too noisy,whether a feature or structure is visible. The output generated by theclassifier is used to determine whether to keep or discard an image. Inother embodiments, a user may review the digital images to determinewhether the image is usable. Additionally or alternatively, the outputof the classifier may be used as feedback to determine if the imageshould be retaken. The feedback may be provided to any user or devicethat is reviewing the digital images. For example, the classifier mayreceive images as they are captured, and the user or device may benotified that a recently captured image should be re-taken.

In an embodiment, if an image set include a low-quality image, then theimage set is removed from the plurality of image sets that are providedas training data. Alternately, if the image set is usable as trainingdata without the low quality image(s), then image set is provided astraining data without the low quality image(s).

For example, assume an image set includes a plurality of lateral imagesand a particular lateral image is a low quality image because thestructure depicted in the image is too obscured. If one or moreremaining lateral images depict a similar view of the structure, thenthe remaining lateral images can still be used as training data.

Using The Trained Machine Learning Model

After the machine learning model is trained, the trained machinelearning model may be used to estimate a measurement of a feature of astructure. The machine learning model receives a particular image set asinput and generates output for the particular image set. The particularimage set may be an image set that was not in the plurality of imagesets used to train the machine learning model. In an embodiment, theparticular image set includes images of the same image type(s) as theimage sets used to train the machine learning model. In an embodiment,the particular image set includes at least the same number of images asthe image sets used to train the machine learning model.

In an embodiment, the machine learning model is trained to estimate ameasurement for a particular type of structure. For example, the machinelearning model may be trained to estimate the perimeter length of awater feature, such as a pool or fountain. The machine learning modelreceives an image set depicting a structure of the particular structuretype and outputs the measurement.

In other embodiments, the machine learning model is trained to determinethe type of structure and estimate a particular type of measurementbased on the type of structure. The machine learning model determinesthe type of structure and outputs the measurement associated with theparticular structure type. In other embodiments, a first machinelearning model is trained to determine the type of structure and asecond machine learning model is trained to estimate a measurement of afeature for the particular type of structure. Additionally oralternatively, a plurality of machine learning models may each betrained to determine a particular type of structure. The plurality ofmachine learning models may be used to determine the type of structure,or to eliminate possible types of structures until a potential type ofstructure is identified.

In an embodiment, the machine learning model is trained to estimate oneor more component variables that are used to calculate the measurement.The machine learning model outputs the one or more component variables.The measurement is calculated based on the one or more componentvariables. In other embodiments, a plurality of machine learning modelsare each trained to output a respective component variable.

Example Process

FIG. 2 illustrates an example process for training a machine learningmodel to estimate a measurement of a feature of a structure. For thepurpose of illustrating a clear example, assume the structure is abuilding and the measurement is the area of the roof.

At step 202, training data is received at a computer system, wherein thetraining data comprises a plurality of image sets and a plurality ofmeasurements. The training data may be received or retrieved from asingle source or from a plurality of sources. For example, the computersystem 110 may receive or retrieve image sets from one or more of dataserver 104, image capture device 106, or database 120.

Each image set of the plurality of image sets comprises one or moredigital images depicting a building. In an embodiment, an image setcomprises an orthographic image and one or more ground level images. Inother embodiments, an image set comprises a single orthographic image.In another embodiment, an image set comprises one or more ground levelimages and no orthographic images. In other embodiments, an image setmay comprise only metadata describing one or more images and no photosor digital image files.

FIG. 3 illustrates an image set, according to an embodiment. Image set300 comprises an orthographic image 310 and a ground-level image 320.Orthographic image 310 depicts a top-down view of building 330,including roof outline 332. Roof outline 332 is an outline of the roofof building 330. Ground-level image 320 depicts the front of thebuilding, including roof pitch 334. Roof pitch 334 is a slope of theroof of building 330.

In the illustrated embodiment, the roof area is calculated or estimatedusing the estimated roof outline and the estimated roof pitch, based ona linear regression. As an example, a_(k) may be one or morecoefficients that are determined by training a machine learning model, xmay be the area of the estimated roof outline, and y may be theestimated pitch of the roof. An example linear regression f(x,y) forcalculating roof square footage may be:

f(x, y)=a ₀ +a ₁ x+a ₂ y+a ₃ xy+a ₄ x ² +a ₅ y ²

In an embodiment, an image set comprises only an orthographic image 310.The roof area may be calculated by dividing the roof outline 332 intoone or more portions and estimating the area of each portion. Forexample, referring to FIG. 3, the top-down view of building 330 depictsa plurality of roof creases 336. The roof creases 336 indicate whereportions of the roof of the building intersect. Roof outline 332 may bedivided into one or more portions based on the roof creases 336.Alternately, a roof pitch may be provided from another source, such as auser, metadata associated with the image, a database or server, or anapplication or computing device that calculates the roof pitchseparately. The roof area may be calculated by using the estimated roofoutline from image 310 and the provided roof pitch.

In an embodiment, an image set comprises a plurality of lateral imagesand no orthographic image. The roof outline may be estimated using theplurality of lateral images. Alternately, the roof outline may beprovided from another source, such as a user, metadata associated withone or more of the lateral images, a database or server, or anapplication or computing device that calculates the roof outlineseparately. The roof pitch is estimated based on one or more of thelateral images. The roof area is calculated using the estimated roofpitch and the roof outline.

At step 204, one or more images in each image set are normalized. Forexample, orthographic image 130 may be scale normalized so that eachimage is the same size and each image corresponds to the same metricarea size.

At step 206, a machine learning model is trained using the trainingdata. For example, computer system 110 executes training instructions111 to train a machine learning model using the plurality of image setsand the plurality of measurements.

In an embodiment, the machine learning model is trained to estimate theroof area directly. The plurality of measurements includes apre-calculated roof area for each image set.

In an embodiment, the machine learning model is trained to estimate aroof outline and a roof pitch. The plurality of measurements include apre-calculated roof outline and a pre-calculated roof pitch for eachcorresponding image set. Alternately, a first machine learning model istrained to estimate the roof outline and a second machine learning modelis trained to estimate the slope of the roof. The first machine learningmodel is trained using at least a portion of each image set in theplurality of image sets and a corresponding roof outline for each imageset. The second machine learning model is trained using at least aportion of each image set in the plurality of image sets and acorresponding roof pitch for each corresponding image set.

At step 208, a particular image set is received. The particular imageset comprises the same number of images of the same image types as theimage sets used to train the machine learning model. The images in theparticular image set depict a particular real-world building.

At step 210, the trained machine learning model is used to generateoutput for particular image set. If the machine learning model wastrained to estimate the roof area directly, then the trained machinelearning model outputs an estimated roof area for the particularreal-world building. If the machine learning model was trained toestimate the roof outline and/or the roof pitch, then the machinelearning model outputs an estimate roof outline and/or estimated roofpitch for the particular real-world building.

In an embodiment, the steps of FIG. 2 may be repeated, such that outputsfrom a first machine learning model at step 210 are used as traininginputs at step 202 or a particular input at step 208 for a secondmachine learning model.

Estimated Measurement Confidence Level

In an embodiment, computer system 110 is further configured to provide aconfidence level associated with the estimated measurement. Theconfidence level estimates how accurate the estimated measurement isexpected to be.

In an embodiment, the confidence level indicates whether the estimatedmeasurement is expected to be within a particular accuracy range. Forexample, computer system 110 may indicate whether the expected errorwill be less than 15%. Additionally, if the estimated measurement is notexpected to be within the particular accuracy range, the user may benotified that the measurement may be inaccurate. Alternately, theestimated measurement may not be provided to the user if it is notwithin the particular accuracy range.

In an embodiment, determining a confidence level comprises comparing theparticular structure depicted in the particular image set with thestructures depicted the training data. The confidence level may be basedon how different the particular structure is from the trainingstructures. Factors for determining differences include, but are notlimited to, structure size, structure shape, expected features of thestructure type.

In the above example, the machine learning model was trained to estimatethe roof area of buildings. For the purpose of illustrating a clearexample, assume the buildings depicted in the training data areprimarily houses of an average size and shape. The confidence level maybe high if the particular image set depicts a traditional house. Theconfidence level may be low if the particular image set depicts anunusually large house and/or an oddly-shaped house.

In an embodiment, determining a confidence level comprises comparing themeasurement estimated by the machine learning model with an expectedmeasurement or an expected range of measurements. In an embodiment, anexpected range of measurement is determined by determining a number ofpixels in an image corresponding to the feature being measured. Thenumber of pixels are used to generate a rough measurement. If theestimated measurement differs from the rough measurement by more than athreshold amount, then the confidence level is low.

In the above example, assume an image set includes an orthographic imagedepicting the roof of a building. The number of pixels comprising thedepicted roof may be counted and used to estimate a rough square footageof the roof If the roof is obscured or if the image is otherwiselow-quality, then the rough measurement would differ significantly fromthe estimated square footage.

In other embodiments, determining a confidence level comprisesdetermining if the particular image set includes a low-quality image asdescribed above for the training data. If the particular image setincludes a low-quality image, then the confidence level is low.

In an embodiment, the confidence level is calculated before using themachine learning model to estimate a measurement. In other embodiments,the confidence level is calculated after the measurement is estimatedusing the machine learning model.

Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A method comprising: training a machine learningmodel to estimate real-world measurements of features of real-worldstructures; wherein training the machine learning model includesproviding to the machine learning model: a plurality of image sets,wherein each image set of the plurality of image sets includes one ormore images of a corresponding real-world structure, and a plurality ofreal-world measurements, wherein the plurality of real-worldmeasurements includes, for each image set of the plurality of imagesets, a real-world measurement of a feature of the correspondingreal-world structure; and after training the machine learning model,using the machine learning model to estimate a particular real-worldmeasurement of a particular feature of a particular real-world structurebased on a particular digital image set containing one or more images ofthe particular real-world structure.
 2. The method of claim 1 whereinestimating the particular real-world measurement is based on apolynomial regression.
 3. The method of claim 1 further comprisingdetermining a confidence level associated with the particular real-worldmeasurement.
 4. The method of claim 1 wherein the particular digitalimage set includes an orthographic photo and one or more lateral photos.5. The method of claim 1 wherein the particular digital image setincludes one or more lateral photos and no orthographic photos.
 6. Themethod of claim 1 wherein training the machine learning model is basedon one or more metadata associated with at least one image in each imageset of the plurality of image sets.
 7. The method of claim 1 wherein theparticular digital image set includes metadata corresponding to the oneor more images and no photos.
 8. The method of claim 1 wherein trainingthe machine learning model is based on output from a second machinelearning model.
 9. The method of claim 1 wherein the particular realworld structure is a particular building and the plurality of image setscorrespond to a plurality of buildings.
 10. The method of claim 9wherein the particular feature of the particular building is a roof ofthe particular building.
 11. The method of claim 10 wherein theparticular real-world measurement is an area of the roof.
 12. The methodof claim 11 wherein using the machine learning model to estimate theparticular real-world measurement comprises: using the machine learningmodel to estimate an outline of the roof; using the machine learningmodel to estimate a slope of the roof; and calculating the area of theroof based on the outline of the roof and the slope of the roof.
 13. Asystem comprising: one or more processors; one or more non-transitorycomputer-readable media storing instructions which, when executed by theone or more processors, cause performance of: training a machinelearning model to estimate real-world measurements of features ofreal-world structures; wherein training the machine learning modelincludes providing to the machine learning model: a plurality of imagesets, wherein each image set of the plurality of image sets includes oneor more images of a corresponding real-world structure, and a pluralityof real-world measurements, wherein the plurality of real-worldmeasurements includes, for each image set of the plurality of imagesets, a real-world measurement of a feature of the correspondingreal-world structure; and after training the machine learning model,using the machine learning model to estimate a particular real-worldmeasurement of a particular feature of a particular real-world structurebased on a particular digital image set containing one or more images ofthe particular real-world structure.
 14. The system of claim 13 whereinestimating the particular real-world measurement is based on apolynomial regression.
 15. The system of claim 13 wherein theinstructions, when executed, further cause determining a confidencelevel associated with the particular real-world measurement.
 16. Thesystem of claim 13 wherein the particular digital image set includes anorthographic photo and one or more lateral photos.
 17. The system ofclaim 13 wherein the particular digital image set includes one or morelateral photos and no orthographic photos.
 18. The system of claim 13wherein training the machine learning model is based on one or moremetadata associated with at least one image in each image set of theplurality of image sets.
 19. The system of claim 13 wherein theparticular digital image set includes metadata corresponding to the oneor more images and no photos.
 20. The system of claim 13 whereintraining the machine learning model is based on output from a secondmachine learning model.
 21. The system of claim 13 wherein theparticular real world structure is a particular building and theplurality of image sets correspond to a plurality of buildings.
 22. Thesystem of claim 21 wherein the particular feature of the particularbuilding is a roof of the particular building.
 23. The system of claim22 wherein the particular real-world measurement is an area of the roof.24. The system of claim 23 wherein using the machine learning model toestimate the particular real-world measurement comprises: using themachine learning model to estimate an outline of the roof using themachine learning model to estimate a slope of the roof; and calculatingthe area of the roof based on the outline of the roof and the slope ofthe roof.